import org.apache.spark.mllib.evaluation.BinaryClassificationMetricsimport org.apache.spark.mllib.linalg.Vectorsimport org.apache.spark.mllib.regression.LabeledPointimport org.apache.spark.mllib.tree.GradientBoostedTreesimport org.apache.spark.mllib.tree.configuration.BoostingStrategyimport org.apache.spark.mllib.tree.model.GradientBoostedTreesModelimport org.apache.spark.sql.{Row, SaveMode}import org.apache.spark.sql.hive.HiveContextimport org.apache.spark.sql.types.{DoubleType, StringType, StructField, StructType}import org.apache.spark.{SparkConf, SparkContext}import scala.collection.mutable.ArrayBufferobject v4score20180123 {  def main(args: Array[String]): Unit = {  val sparkConf = new SparkConf().setAppName("v4model20180123")  val sc = new SparkContext(sparkConf)  val hc = new HiveContext(sc)  val dataInstance = hc.sql(s"select * from lkl_card_score.fqz_score_dataset_04vals").map {    row =>      val arr = new ArrayBuffer[Double]()      //剔除label、phone字段      for (i <- 3 until row.size) {        if (row.isNullAt(i)) {          arr += 0.0        }        else if (row.get(i).isInstanceOf[Int])          arr += row.getInt(i).toDouble        else if (row.get(i).isInstanceOf[Double])          arr += row.getDouble(i)        else if (row.get(i).isInstanceOf[Long])          arr += row.getLong(i).toDouble        else if (row.get(i).isInstanceOf[String])          arr += 0.0      }      (row(0),row(1),row(2),Vectors.dense(arr.toArray))  }  val  modeltest=GradientBoostedTreesModel.load(sc,s"hdfs://ns1/user/songchunlin/model/v4model20180123s")  val preditDataGBDT = dataInstance.map { point =>    val prediction = modeltest.predict(point._4)    //order_id,apply_time,score    (point._1,point._2,point._3,prediction)  }  preditDataGBDT.take(5)  //rdd转dataFrame  val rowRDD = preditDataGBDT.map(row => Row(row._1.toString,row._2.toString,row._3.toString,row._4))  val schema = StructType(    List(      StructField("order_id", StringType, true),      StructField("apply_time", StringType, true),      StructField("label", StringType, true),      StructField("score", DoubleType, true)    )  )  //将RDD映射到rowRDD，schema信息应用到rowRDD上  val scoreDataFrame = hc.createDataFrame(rowRDD,schema)  scoreDataFrame.count()  scoreDataFrame.write.mode(SaveMode.Overwrite).saveAsTable("lkl_card_score.fqz_score_dataset_03val_v4_predict0123s")}}